Adaptive Batch Size Image Merging Steganography and Quantized Gaussian Image Steganography

Sharifzadeh, Mehdi; Aloraini, Mohammed; Schonfeld, Dan

doi:10.1109/tifs.2019.2929441

Cited by 63 publications

(53 citation statements)

References 26 publications

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“…Additionally, we prove that the achieved maximum embedding rate comes in agreement with the well-known square root law (SRL) of steganography [4,Ch.13]. Finally, we introduce comparisons between our theoretically achieved maximum embedding rate with other practically calculated rates provided by [10], [16], and [17]. The results demonstrate that our calculated upper bound is relatively very small compared to the referenced practical stenographic methods.…”

Section: Introductionsupporting

confidence: 63%

“…Previous approaches of steganography concerned with the definition of the cost function are either not globally accurate for all multimedia types or accurate with limited scope for a certain type of multimedia for a certain class of embedding or detection techniques [10]. Additionally, due to the complexity of Gibbs distribution, previous approaches either use more relaxed mathematical distributions such as Gaussian distribution [10]- [17] in the statistical modeling or presume a preposition on the maximum embedding rate then try to show the correctness of the preposition [11], [12]. Thus the estimated performance may not reflect the real one due to the model relaxation.…”

Section: Introductionmentioning

confidence: 99%

“…Although [16], [17] provided improved results and an analytical formulation for the relation between the embedding rate and the cost function, the provided results are based on a relaxed statistical model for the cover. Another approach in [10] focused on gray-scale images as cover objects with an initial assumption that the cover, the message, and the stego-object to be statistically modelled as a Multivariate-Quantized-Gaussian-Distribution. This approach provides a theoretical limit for three popular detection strategies for the likelihood ratio test: Bayes, Minimax, and Neyman-Pearson.…”

mentioning

confidence: 99%

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Information-Theoretic Limits for Steganography in Multimedia

Mohamed¹,

Abdelaziz²,

Elliethy³

et al. 2021

Preprint

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<pre>Steganography in multimedia aims to embed secret data into an innocent multimedia cover object. The embedding introduces some distortion to the cover object and produces a corresponding stego-object. The embedding distortion is measured by a cost function that determines the probability of detection of the existence of secret embedded data. An accurate definition of the cost function and its relation to the maximum embedding rate is the keystone for the proper evaluation of a steganographic system. Additionally, the statistical distribution of multimedia sources follows the Gibbs distribution which is a complex statistical model that prohibits a thorough mathematical analysis.</pre><pre>Previous multimedia steganographic approaches either assume a relaxed statistical distribution of multimedia sources or presume a proposition on the maximum embedding rate then try to prove the correctness of the proposition. Alternatively, this paper introduces an analytical procedure for calculating the maximum embedding rate within multimedia cover objects through a constrained optimization problem that governs the relationship between the maximum embedding rate and the probability of detection by any steganographic detector. In the optimization problem, we use the KL-divergence between the statistical distributions for the cover and the stego-objects to be our cost function as it upper limits the performance of the optimal steganographic detector. To solve the optimization problem, we establish an equivalence between the Gibbs and the correlated-multivariate-quantized-Gaussian distributions for mathematical thorough analysis. The solution to our optimization problem provides an analytical form for the maximum embedding rate in terms of the WrightOmega function. Moreover, we prove that the achieved maximum embedding rate comes in agreement with the well-known square root law (SRL) of steganography. We also establish the relationship between the achieved maximum embedding rate and the experimental results obtained from several embedding and detection steganographic techniques.</pre>

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Section: Introductionsupporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Information-Theoretic Limits for Steganography in Multimedia

Mohamed¹,

Abdelaziz²,

Elliethy³

et al. 2021

Preprint

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“…In watermarking, the main objective is robustness. It should not be confused with steganography where imperceptibility is the main goal [45].…”

Section: Digital Watermarkingmentioning

confidence: 99%

A Survey on Piracy Protection Techniques in Digital Cinema Watermarking Schemes

Zolfaghari¹,

Mitra²

2020

Recent Trends in Communication Networks

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Watermarking is used in several areas such as CDNs (Content Delivery Networks), as part of the rights management system for counterfeit prevention. Watermarking schemes need some additional features in order to be used in digital cinema. In fact, extra watermarks are added to movies by cinema projectors in projection time, which help identify the cinema hall in which the illegal copy has been recorded. But distortions caused by hand vibrations and the point of view angle make it difficult to recover the watermark. This makes it necessary to be distortionresistant for the watermarking schemes used in digital cinema. On the other hand, theatre owners would like to locate the camcorder that has recorded the pirate copy. This requires watermarking schemes to be able to estimate the distance and angle using the distributed pirate copy. In this chapter, we present a review on watermarking techniques specifically designed to attack the aforementioned problems.

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“…The adaptive steganography assigns a cost to each modified element in the cover image and the impact of embedding modifications will be measured using a distortion function. The secret information is embedded while minimising the distortion function [3, 4]. Adaptive steganography is an important aspect of steganography.…”

Section: Introductionmentioning

confidence: 99%

Reversible data hiding in encrypted images for coding channel based on adaptive steganography

Chen

Wang

et al. 2020

IET image process

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In this study, a novel reversible data hiding (RDH) in encrypted domain scheme for coding channel based on slidingblock segmentation and adaptive steganography is proposed. The proposed scheme enriches the residual information with as little additional encryption information as possible to improve the testing error rate of a steganalyser by sliding-block segmentation with bit stream encryption. The specific encryption process effectively weakens the correlation between the adjacent pixels and minimises the size of key stream bits. The encryption key can be further embedded in the channel code stream before transmitted in the channel. Experimental analysis shows that the image encrypted by the proposed RDH scheme can achieve a peak-signal-to-noise ratio of >50 dB, as the payload is 0.5 bits per pixel (bpp). In terms of security performance, compared with the state-of-the-art methods, their method has a higher testing error rate when the steganalyser is utilised. Even if the payload is 0.5 bpp, the testing error rate is >0.25.

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Adaptive Batch Size Image Merging Steganography and Quantized Gaussian Image Steganography

Cited by 63 publications

References 26 publications

Information-Theoretic Limits for Steganography in Multimedia

Information-Theoretic Limits for Steganography in Multimedia

A Survey on Piracy Protection Techniques in Digital Cinema Watermarking Schemes

Reversible data hiding in encrypted images for coding channel based on adaptive steganography

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